Three Thousand Seven Hundred Thirty-Eight Posttraumatic Pulmonary Emboli
Bibliographic record
Abstract
OBJECTIVE: This study was undertaken to determine the current incidence of pulmonary embolism (PE) and its attributable mortality after injury. BACKGROUND: Despite compliance with prophylactic measures, PE remains a threat to postinjury recovery. We hypothesized that the liberal use of chest computed tomography after injury has resulted in an increased rate of detection of PE but that the mortality attributable to PE has decreased over the past decade. We also postulated that the risk factors for posttraumatic PE might be different from those for deep venous thrombosis (DVT). METHODS: We examined demographics, injury data, risk factors, and outcomes from patients with DVT and PE compiled in the recent years (2007-2009) in the National Trauma Data Bank (NTDB). For comparison, we used patient data entered into NTDB from 1994 to 2001. Statistical models were created to examine the predictors of DVT and PE and PE-related mortality. RESULTS: Among 888,652 patients in the current NTDB cohort, there were 9398 episodes of DVT (1.06%) and 3738 of PE (0.42%). Although many risk factors overlapped, a severe chest injury (Abbreviated Injury Score ≥ 3) conferred a much higher risk of PE than DVT. When comparing results from centers that had contributed to both data sets, there was a more than 2-fold increase in PE occurrence in the current cohort (0.49% vs 0.21%, P < 0.01) but with a significant reduction in PE-adjusted mortality (odds ratio, 4.08 vs 2.42). CONCLUSIONS: The reported incidence of PE after trauma has more than doubled in recent years, while the PE-associated mortality has significantly decreased, suggesting that we are identifying a different disease entity or stage. Chest injuries convey a substantial risk for PE, a risk not likely to be diminished by leg compression devices or vena cava filters.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".